Guiding an HPSG Parser using Semantic and Pragmatic Expectations 
Jim Skon 
Computer and Information Science Department 
The Ohio State University 
Columbus, OH 43210, USA 
Internet: skon@ cis.ohio-state.edu 
Abstract 1 
Efficient natural language generation has been successfully 
demonstrated using highly compiled knowledge about speech 
acts and their related social actions. A design and prototype 
implementation of a parser which utilizes this same pragmatic 
knowledge to efficiently guide parsing is presented. Such 
guidance is shown to prune the search space and thus avoid 
needless processing of pragmatically unlikely constituent 
structures. 
INTRODUCTION 
The use of purely syntactic knowledge during the parse 
phase of natural language understanding yields considerable 
local ambiguity (consideration of impossible subeonstituents) 
as well global ambiguity (construction of syntactically valid 
parses not applicable to the socio-pragmatic context). 
This research investigates bringing socio-pragmatic 
knowledge to bear during the parse, while maintaining a 
domain independent grammar and parser. The particular 
technique explored uses knowledge about the pragmatic context 
to order the consideration of proposed parse constituents, thus 
guiding the parser to consider the best (wrt the expectations) 
solutions first. Such a search may be classified as a best- 
first search. 
The theoretical models used to represent the pragmatic 
knowledge in this study are based on Halliday's Systemic 
Grammar and a model of the pragmatics of conversation. The 
model used to represent the syntax and domain independent 
semantic knowledge is HPSG - Head-driven Phrase Structure 
Grammar. 
BACKGROUND 
Patten, Geis and Becker (1992) demonstrate the 
application of knowledge compilation to achieve the rapid 
generation of natural language. Their mechanism is based on 
Halliday's systemic networks, and on Geis' theory of the 
pragmatics of conversation. A model of conversation using 
principled compilation of pragmatic knowledge and other 
linguistic knowledge is used to permit the application of 
pragmatic inference without expensive computation. A 
pragmatic component is used to model social action, including 
speech acts, and utilize conventions of us.g involving such 
features of context such as politeness, ~e~gister, and stylistic 
features. These politeness features are critiqd}l to the account of 
indirect speech acts. This pragmatic knovCledge is compiled 
into course-grained knowledge in the form of a classification 
hierarchy. A planner component uses knowledge about 
conditions which need to be satisfied (discourse goals) to 
produce a set of pragmatic features which characterize a desired 
utterance. These features are mapped into the systemic 
l Research Funded by The Ohio State Center for Cognitive 
Science and The Ohio State Departments of Computer and 
Information Science and Linguistics 
grammar (using compiled knowledge) which is then used to 
realize the actual utterance. 
The syntactic/semantic component used in this study is a 
parser based on the HPSG (Head Driven Phrase Structure 
Grammar) theory of grammar (Pollard and Sag, 1992). HPSG 
models all linguistic constituents in terms of part/a/ 
information structures called feature structures. 
Linguistic signs incorporate simultaneous representation of 
phonological, syntactic, and semantic attributes of 
grammatical constituents. HPSG is a lexiealized theory, 
with the lexical definitions, rather then phrase structure rules, 
specifying most configurational constraints. Control (such as 
subcategorization, for example) is asserted by the use of HPSG 
constraints - partially filled in feature structures called feature 
descriptions, which constrain possible HPSG feature structures 
by asserting specific attributes and/or labels. 
A HPSG based chart parser, under development at the 
author's university, was used for the implementation part of 
this study. 
FEATURE MAPPING 
Planning & generation of coherent "speech" in a 
conversation requires some understanding of the "hearer's" 
perspective. Thus the speaker naturally has some (limited) 
knowledge about possible responses from the hearer. This 
knowledge can be given to the same planner used for 
generation, producing a partial set of pragmatic features or 
expectations. These pragmatic expectations can then be 
mapped into the systemic grammar, producing a set of 
semantic and syntactic expectations about what other 
participants in the conversation will say. 
The technique explored here is to bring such expectations 
to bear during the parse process, guiding the parser to the most 
likely solution in a best-first manner. It is thus necessary that 
the generated expectations be mapped into a form which can be 
directly compared with constituents proposed within the HPSG 
parse. 
Consider the sentence "Robin promised to come at 
noon", with the following context: 
Sandy: "I guess we should get started, what time did they 
say they would be here?" 
Kim: "Robin promised to come at noon" 
A set of plausible partial expectations generated by the 
pragmatic and systemic components in anticipation of Kim's 
response might be: 
((S) (UNMARKED-DECLARATIVE)) 
((S SUBJECT) (PROPER)) 
((S BETA) (NONFINITEPRED)) 
((S PREDICATOR) (PROMISED)) 
((S BETA TEMPORAL) (PP)) 
((S BETA PREDICATOR) (ARRIVAL)) 
In these expectations the first list of each pair (e.g. (S BETA)) 
represents a functional role within the expected sentence. The 
295 
second list in each pair are sets (in this case singleton) of 
expected features for the associated functional roles. These 
expected features assert expectations which are both semantic 
(e.g. PROMISED) and syntactic (e.g. ((S BETA 
TEMPORAL) (PP)) asserts both the existance and location of 
a temporal adjunct PP). 
Note that in these expectations the temporal adjunct "at 
noon" should modify the embedded clause "to come", as would 
be expected in the specified context. 
Next consider the possible HPSG parses of the example 
sentence. Figures 1 and 2 below illustrate two semantically 
distinct parses generated by our HPSG parser. 
S H 
s// H/ vP 
NP V V V PP 
Robin promised to come at noon 
Figure 1. 
S H 
/ / ,V-- vr \ NP V V V PP 
Robin promised to come at noon 
Figure 2. 
Mapping expected features into HPSG constraints: 
Features generated from pragmatic expectations can be 
mapped into constraints on HPSG structures, stated in terms 
of feature descriptions. Below are the HPSG feature 
descriptions corresponding to the pragmatically generated 
features PP and UNMARKED-DECLARATIVE. 
PP = SYNSEMILOCICAT HEAD prep . \[MARKING unmarked\]\] 
phrase cat ' 
Figure 3. 
UNMARKED-DECLARATIVE = 
FDTRSIHEAD-DTRISYNSEM v_E 
phraseLSU~-DTRISYNSEMILOCICATIH EAD __ 
Figure 4. 
noun 
Mapping expected functional roles into HPSG 
constituent structure: 
Pragmatic expectations are expected within certain 
functional roles, such a SUBJECT, PREDICATOR, BETA 
(the embedded clause) etc. This structural information must be 
used to assert the constraints into the relevant HPSG 
substructures. This mapping is not as straightforward as the 
feature mapping technique, as the structure induced by the 
systemic grammar is "flatter" than the structure produced by 
HPSG. 
Consider the following pragmatically generated 
expectation: 
((S TEMPORAL) (PP)) 
Such an expectation may be realized by great variety of 
HPSG structural realizations, e.g.: 
1. Kim ran at noon 
2. Kim could run home at noon 
3. K.im could have been running home at noon 
4. Kim ran east at noon. 
In these examples modal verb operators (1-3) and multiple 
adjuncts (4) vary the actual structural depth of the temporal PP 
within the HPSG model. Thus a given systemic role path 
may have numerous HI~G constituent path realizations. One 
possible mapping technique is to generate constraints 
expressing all possible HPSG structural variants. This, 
however would lead in many cases to a combinatorial 
explosion of constraints. The technique employed by this 
study was to add a new clause attribute to verbal HPSG signs, 
and use this attribute to embed within the signs a "clausally 
flattened" structures. Each HPSG verbal sign in the same 
clause structure shares the same clausal value. The clause 
value is a structure with labels for each systemic role, where 
each label points to the constituent which fills that role in the 
given verbal clause. A clausal boundry is said to exist 
between distinct clausal domains. A clausal structure is 
illustrated in figure 5: 
~"~vPIF~I 
VI~ v\[r~" I \] V/- / ~ %P\[ \[~\] P 
R°bin promlised "E" H\[~ H//~p 
v\[N\] I I come at noon 
\[C F PREDICATOR V\[pr°mised\] \]\] \[\] LAUSE | SUBJECT NP\[Robin\] 
=- BETA VP\[to come at noon\]\] 
rEI~LAUSE r PREDICATOR V\[come\] LTEMPORAL PP\[atnoon\]\] \]\] 
I= 
Figure 5. 
The current mapping only considers the mapping of roles 
within verbal signs. Similar role structures may exist for 
other constituent types, such as for noun phrase. Thus far the 
verbal clause boundary definition has been adequate for other 
phrasal structures. 
GUIDING THE HPSG PARSE 
The guidance strategy employed is to evaluate all 
proposed edges (i.e. complete and partially complete 
constituents) against the expectations, ranking each based on 
the relative similarity with the expectations. These edges are 
296 
then placed in an agenda (a list of priority queues) and 
removed from the agenda and included in the partial parse in a 
best first order. 
Critical to the success of a best-first algorithm is the 
heuristic evaluation function used to order the proposed 
constituents. 
The heuristic evaluation function: 
The heuristic evaluation function is based on three specific 
types of tests: 
I. Role match - does a constituent match a role's set of 
expected features? 
II. Role path match - is a constituent role path compatible 
with the roles of its children? 
III. Clausal completeness - are all clausal roles expected for 
this constituent present? 
Tests II and III above require that constituents under 
consideration have roles already assigned to them. For 
example, in the case of II, the test requires roles for both the 
new constituent and the proposed daughters of the constituent. 
But since the parse strategy employeed is bottom-up, role 
paths cannot be anchored to a root, and thus fully known, until 
parse completion. The solution to this dilemma is to 
hypothesise a constituent's role using a process similar to 
abduction. Two types of knowledge are exploited in this 
process. First, roles with features which subsume or are 
consistant with a proposed constituent are considered good 
candidate roles. Also, roles may also be inferred by projecting 
up from the roles already hypothesized for the children. By 
intersecting these two sources of role evidence, the list of 
hypothesized roles can be refined (by ruling out roles without 
both types of evidence). In this manner the hypothesized roles 
of later constituents can be refined from descendant 
constituents. In the case of roles projected from daughters, 
clausal boundary knowledge must be applied to correctly infer 
the parent role. 
EVALUATION & TESTING 
The techniques described here have been used successfully 
to guide the parsing of several sentences taken from real 
conversations. The pragmatic and semantic knowledge already 
existed from Patten's research (Patten, 1992) to generate these 
sentences. A subset of this knowledge, judged to represent the 
partial knowledge available to a listener, was used to generate 
expectations in the form described above. 
The parser used in this study by default produced all 
possible parses. The modified version attempts to converge on 
the "expected" parse first, and terminate. For each sentence 
tested the parser converges on the correct parse first. When the 
expectations are modified to expect a different parse, a different 
(and correct) parse is found first. The results in terms of 
speedup vary considerably depending on the level of ambiguity 
present in the sentence. The most complex sentence parsed 
thus far exhibits considerable speedup. When unguided, the 
parser produces 24 parses, and considers a total of 252 distinct 
constituents. In the guided case, the parser only considers 39 
constituents, and converges on the one "correct" parse first. 
Within the current testing environment, this guidence results 
in a greater then ten-fold speedup in terms of CPU time. 
SUMMARY 
Pragmatic knowledge about language usage in routine 
conversational contexts can be highly compiled. This 
knowledge can be used to produce semantic and syntactic 
expectations about next turns in conversation, especially of 
next turns that are second members of adjacency pairs 
(Schegloff & Sacks 1973). By mapping expected features into 
HPSG constraints, and by augmenting HPSG sign structures 
to model the role structure of systemic grammar, these 
expectations can be used as constraints on possible constituent 
structures of a HPSG constituent. Given this mapping, the 
expectations may then be used to order the parse process, 
guiding the parse, and avoiding the consideration of 
pragmatically unlikely constructions. This process reduces the 
number of constituents considered during parsing, reducing 
parse time and permitting the parser to correctly select the 
parse most like the pragmatic expectations, 
This solution closely follows a classical A.I. search 
technique called a best-first search. The heuristic evaluation 
function used to classify the proposed constituents for best 
first ordering uses inference similar to abductive reasoning. 
One benefit of this solution is that it retains the 
modularity of the syntactic and semantic components, not 
requiring a specialized grammar for each contextual domain. In 
additional, as the coverage of the grammar increases, the search 
space will also increase, and thus possible benefits increase. 
Work is continuing on this study. Currently the heuristic 
is being enhanced to consider the specificity of an expectation 
match, ordering those edges which match the most specific 
features first. In addition, work is in progress to extend the 
coverage of the grammar and mapping to include the 
conversation domain utilized in Patten, Geis & Becker 1992. 

References 
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